IntroToNeuralNetworks - Basics - 1

One should look for what is and not what he thinks should be. (Albert Einstein)

Basics: Topic introduction

In this part of the course, we will cover the following concepts:

  • Introduction to neural networks
  • Neural networks use cases

Module completion checklist

Objective Complete
Describe neural networks and identify their use cases
Discuss the architecture of a simple neural network and identify its components

Neural networks: introduction

  • Our brain uses neural networks to process information around us with the help of neurons that respond to the input from the environment
  • An artificial neural network (ANN), often called a neural network (NN), is a host of well-known machine learning algorithms that attempt to mimic the neural network a human brain uses
  • It uses interconnected nodes or neurons in a layered structure that resembles the human brain

Meet A.N.N


centered

Breakout: neural networks use cases

  • Neural network is a popular method and several industries are using it to perform different tasks

  • Read the article assigned to your group and discuss the following:

    • How this could be related to your work?
    • How can neural networks help in your current role or field?

  • Nominate a representative to share the the synopsis of your discussion


  1. Adopting AI in Drug Discovery

  2. How Neural Networks help delivery Robots navigate

  3. Neural Networks for Marketing


centered

Use cases by data type

  • Let’s see what type of data can be used for different purposes:
Data type Use case
Raw text Analyze emails and chats from customer support database to detect most common issues customers face
Audio data Help identify whether a person is under distress by analyzing recorded phone calls to a hotline
Stock prices Determine price trends, detect anomalies, asses risk
Sensor data Insight into preventing fatalities, based on data collected from car sensors

Use cases by algorithm type

Algorithm type Use case
Classification This image represents a horse; this email looks like spam; this transaction is fraudulent
Clustering These two sounds are similar; these documents belong to the same topic / domain
Recommender systems Given their web activity, this customer looks like they are going to stop using your service



  • Chat question: Can you think of use cases from projects that you have been working on?

Module completion checklist

Objective Complete
Describe neural networks and identify their use cases

✔

Discuss the architecture of a simple neural network and identify its components

Simple neural network

  • Now that we’ve discussed the use cases of neural networks, we will walk through a simple neural network architecture and discuss its components


  • Note: Later in the course, we will go through a more complex neural network called deep learning

Perceptron

  • A perceptron, a fundamental concept in machine learning, is a type of artificial neural network
  • The image below depicts a perceptron, a neuron, connected with n other neurons
  • It is the simplest type of neural network and we will discuss each of the components in the subsequent slides

centered

Neural networks: layers

  • The major components of a neural network are the:

    • inputs
    • weights
    • summation processor
    • activation function
    • outputs

centered

Inputs

centered

Weights

centered

Summation processor

centered

  • Before the summation processor “squeezes,” the computation goes through one or more hidden layers (this is where the black box concept of a neural network comes from)
  • A simple neural network will have up to two hidden layers, and anything over two hidden layers becomes a type of deep learning network

Activation function

centered

  • There are many types of activation functions such as ReLU, sigmoid, tanh, and softmax
  • Based on the inputs, the function derives its current state, and outputs an activation value

Outputs

centered

Neural networks: architecture

centered


  • Note: There is no single NN architecture that works well for all problems!
  • The general architecture of a simple NN looks like a piece of layered cake
  • Depending on the number of hidden layers, layer type and its configuration (e.g., the number of neurons in it, the activation function and other parameters), each NN architecture might yield drastically different results for the same problem
  • Choosing the right NN architecture for the problem and tuning the model is instrumental to successful NN implementation

Deep learning vs. simple neural networks

  • Typically, one or two hidden layers are enough for most problems
  • Deep learning is a branch of machine learning that uses deep neural networks (DNNs)
  • DNNs are essentially the same as ANNs, with more complex architecture that calls for the use of multiple layers
  • Here is a diagram of how the two differ architecturally:

centered

Knowledge check

centered


Module completion checklist

Objective Complete
Describe neural networks and identify their use cases

✔

Discuss the architecture of a simple neural network and identify its components

✔

Basics: Topic Summary

In this part of the course, we have covered:

  • Introduction to neural networks
  • Neural networks use cases

Congratulations on completing this module!

icon-left-bottom